IVE uses a vision-language model to imagine, verify, and execute new tabletop arrangements, collecting more diverse exploration data than RL baselines and supporting downstream policy and world-model learning.
Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach
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abstract
Hand-drawn maps can be used to convey navigation instructions between humans and robots in a natural and efficient manner. However, these maps can often contain inaccuracies such as scale distortions and missing landmarks which present challenges for mobile robot navigation. This paper introduces a novel Hand-drawn Map Navigation (HAM-Nav) architecture that leverages pre-trained vision language models (VLMs) for robot navigation across diverse environments, hand-drawing styles, and robot embodiments, even in the presence of map inaccuracies. HAM-Nav integrates a unique Selective Visual Association Prompting approach for topological map-based position estimation and navigation planning as well as a Predictive Navigation Plan Parser to infer missing landmarks. Extensive experiments were conducted in photorealistic simulated environments, using both wheeled and legged robots, demonstrating the effectiveness of HAM-Nav in terms of navigation success rates and Success weighted by Path Length. Furthermore, a user study in real-world environments highlighted the practical utility of hand-drawn maps for robot navigation as well as successful navigation outcomes compared against a non-hand-drawn map approach.
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cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models
IVE uses a vision-language model to imagine, verify, and execute new tabletop arrangements, collecting more diverse exploration data than RL baselines and supporting downstream policy and world-model learning.